Search Results for author:
Found 15 papers, 4 papers with code
Predicting risk of cardiovascular disease using retinal OCT imaging
A Random Forest (RF) classifier was subsequently trained using the learned latent features and participant demographic and clinical data, to differentiate between patients at risk of CVD events (MI or stroke) and non-CVD cases.
Radiology Report Generation Using Transformers Conditioned with Non-imaging Data
While recent deep-learning approaches for automated report generation from medical images have seen some success, most studies have relied on image-derived features alone, ignoring non-imaging patient data.
Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation
Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images.
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
Adaptive Semi-Supervised Segmentation of Brain Vessels with Ambiguous Labels
Accurate segmentation of brain vessels is crucial for cerebrovascular disease diagnosis and treatment.
Learning disentangled representations for explainable chest X-ray classification using Dirichlet VAEs
We generate visual examples to show that our explainability method, when applied to the trained DirVAE, is able to highlight regions in CXR images that are clinically relevant to the class(es) of interest and additionally, can identify cases where classification relies on spurious feature correlations.
Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology
Here, we propose a new framework for gene discovery entitled Unsupervised Phenotype Ensembles (UPE).
Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images
In such scenarios, enforcing smooth, globally continuous deformation fields leads to incorrect/implausible registration results.
The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays
Model performance results have been exceptional when training and testing on open-source data, surpassing the reported capabilities of AI in pneumonia-detection prior to the COVID-19 outbreak.
Flip Learning: Erase to Segment
Weakly-supervised segmentation (WSS) can help reduce time-consuming and cumbersome manual annotation.
Style Curriculum Learning for Robust Medical Image Segmentation
A style transfer model with style fusion is employed to generate the curriculum samples.
Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction
In this paper, we propose a novel approach to sensorless freehand 3D US reconstruction considering the complex skill sequences.
A Deep Discontinuity-Preserving Image Registration Network
Image registration aims to establish spatial correspondence across pairs, or groups of images, and is a cornerstone of medical image computing and computer-assisted-interventions.
CAR-Net: Unsupervised Co-Attention Guided Registration Network for Joint Registration and Structure Learning
Image registration is a fundamental building block for various applications in medical image analysis.
Generalize Ultrasound Image Segmentation via Instant and Plug & Play Style Transfer
Deep segmentation models that generalize to images with unknown appearance are important for real-world medical image analysis.
